Curriculum Vitae

Google Scholar Citation

Web of Science ResearcherID

Among the most popular and most cited articles published in the last three years (2023-2026) at JMVA:  #137

Among the most cited articles published  in the last three years (2022-2024) at JCGS: #127

Among the most popular articles published  in the last three years (2021-2024) at JMVA: #116

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List of Publications (Updated: February 8th, 2026)

2026 

• 175. Correia, A.P*., Diniz, C.A.R, and Lachos V.H. (2026+). Matrix Variate Skew Normal Distribution: Properties and Estimation. Statistical Analysis And Data Mining (Under Review).
• 174. Tomarchio S.D. &  Lachos V.H. (2026+).  Finite Mixtures of Matrix-Variate Normal Distributions on Censored and Missing Data. Metron ( Under Review).
• 172. Diniz, C. and Lachos, V.H. (2026+). Finite mixtures of matrix variate generalized asymmetric Laplace distribution for three-way data. Journal of Computational and Applied Mathematics (Under Review).
• 171. Diniz, C. Choi, J.* and Lachos, V.H. (2026+). An EM algorithm for fitting matrix-variate Student’s-t distributions on interval-censored and missing data. Journal of Multivariate Analysis (Under Review)
• 170. Fabio, L., Carrasco, J.M., Lachos, V. H. and Chen, M-H (2026+). Likelihood-based inference for joint modeling of correlated count and binary outcomes with extra variability and zeros. The Canadian Journal of Statistics (Under Review).
• 169. Schumacher, F. L., Lachos, V.H. and Matos, L. A. (2026+). Linear Mixed Models for Complex Longitudinal Data with Applications in R.  SpringerBriefs in Statistics Series. (Book, Under Review).
• 168. Yang, F*., Feng, R. and Lachos, V.H. (2026+). Comparison of Zero-Inflated and Hurdle INAR(1) Processes for Modeling Count Data. Brazilian Journal of Probability and Statistics (In Press).
• 167. Lim, H.*, Ordoñez, J.A.*,  Punzo, A. and  Lachos, V.H. (2026+). Heckman selection – contaminated normal model. Journal of Computational and Graphical Statistics (In Press).
• 166.  Nbreka, B.*,  Dey, D. and  Lachos, V.H. (2026+). Bayesian Estimation of Contagion Effect: An Application of Friendship Networks and Alcohol Behavior. The New England Journal of Statistics in Data Science (In Press).
• 165. Oliveira, D.C.R., Schumacher, F. & Lachos, V.H. (2026+). The use of the EM algorithm for regularization problems in high-dimensional linear mixed-effects models.  Statistical Methods in Medical Research (In Press).
• 164.  Carrasco, J.M., Castillo, L.F. , Dey,  D. and Lachos, V.H. (2026+). Variable selection in high-dimensional generalized linear mixed models for correlated binary data. Statistics in Biosciences (In Press).
• 163. Lim, H.*, Lachos, E.V. and Lachos V.H. (2026). Bayesian analysis of flexible Heckman-selection models using Hamiltonian Monte Carlo. Computational Statistics, 41, 40.
• 162.  Schumacher, F.L, Matos, L A. &  Lachos, V.H. (2026). skewlmm: An R Package for Fitting Skewed and Heavy-Tailed Linear Mixed Models. Journal of Statistical Software, 115(7), 1–32.

2025

• 161. Wang,  W-L., Lachos, V.H., Chen, Y-C*. &  Lin, T-I. (2025). Flexible clustering via Gaussian parsimonious mixture models with censored and missing values. Test, 34, 431–458.
• 160. Zhong*, K., Schumacher, F. Castro, M. & Lachos, V.H. (2025). Bayesian analysis of censored linear mixed-effects models for heavy-tailed irregularly observed repeated measures—Statistics in Medicine, 44: e10295.
• 159. Benites, L. E.*, Bolfarine, H., Lachos, V.H. and Zeller, C.B. (2025). Finite mixture of regression models based on multivariate scale mixtures of skew-normal distributions, Computational Statistics, 40, 5163–5194.
• 158. Oliveira, M.S., Galarza, C., Prates, M.O. and Lachos, V.H. (2025). Influence diagnostics in the Heckman selection models based on EM algorithms. Journal of Applied Statistics, 52(13), 2384-2412.
• 157. Jorge L. Bazán, Alex de la Cruz*  and V. H. Lachos (2025). A new class of binary regression models for unbalanced data with applications in medical data. Sankhya A, 87, 699-740.
• 156. Gil, Yessenia A.*, Garay, A. W. and Lachos, V.H. (2025). Likelihood-based inference for interval censored regression models under heavy-tailed distributions. Statistical Methods & Applications, 34, 519–544.
• 155.  Liu, D.*,  Oliveira, D.C.R.,  Castro, L.M. and Lachos, V.H. (2025). Lasso regularization for censored regression and high-dimensional predictors. Journal of Statistical Computation and Simulation, 95, 2724-2744.
• 154. Galarza, C.E.  and Lachos, V.H. (2025). Truncated Moments. International Encyclopedia of Statistical Science, pp. 2810–2814.
• 153. Zhong, K.*, Zhang P., Castro, L.M.,  and Lachos, V.H. (2025). Bayesian analysis of autoregressive linear mixed models for censored responses using the multivariate-t distribution. Japanese Journal of Statistics and Data Science, 8, 743–770.
• 152. Zhong, K.* Olivari*, R.C. , Garay, A.M. and Lachos, V.H. (2025). Mixed-effects models for censored data with autoregressive errors using the multivariate Student’s t-distribution.  The New England Journal of Statistics in Data Science, 3(1), 119-134.
• 151. Jerez-Lillo, N.*, Tapia, A., Lachos, V.H. and Ramos, P.L. (2025). A new semi-parametric power-law regression model with long-term survival, change point detection, and regularization. Statistics in Medicine, 44:e70043.
• 150. Lachos, V.H., Tomarchio, S.D., Punzo A. and Ingrassia, S. (2025). An EM algorithm for fitting matrix-variate normal distributions on interval-censored and missing data. Statistics and Computing, 35:39.
• 149. Padilla*, J.L., Azevedo, C.L. and Lachos, V.H. (2025). Parameter recovery for a skew multidimensional item response model: a comparison of MCMC algorithms and measurement of some effects of interest. Journal of Statistical Computation and Simulation, 95, 467-489.
• 148. Conceição, K.S., Andrade, M.G., Lachos, V.H. and Ravishanker, N. (2025).  Bayesian inference for zero-modified power series regression Models, Mathematics. 13: 60.

2024

• 147. Garay, A.W., Medina, F.L., de Freitas*, S.T. and Lachos, V.H. (2024). Bayesian analysis of censored/missing regression models with autoregressive errors and symmetrical distributions. Statistical Papers, 65, 5649–5690.
• 146. Park*,  J., Dey, D. and Lachos, V.H. (2024). Finite mixture of regression models for censored data based on the skew-t distribution.  Computational Statistics, 39, 3695–3726.
• 145. Ordonez*, J.A, Galarza, C. E. and Lachos, V.H. (2024). Spatial censored regression models in R: The censSpatial package.  Preprint arXiv:2110.05570.  SoftwareX, 27, 101762.
• 144. Schumacher, F. L., Lachos, V.H.  Castro, L.M.C. and Matos, L. A. (2024).  A censored time series analysis for responses on the unit interval: An application to acid rain modeling. Sankhya A,  86, 637–660.
• 143. Ordoñez*, A.C., Prates, M.O.  Matos, L.A. and Lachos, V.H. (2024). Objective Bayesian analysis for spatial Student-t regression models. Journal of Spatial Science, 69, 61-79.
• 142. Ordoñez*, A.C., Prates, M.O.  Bazan, J.L. and Lachos V.H. (2024). Penalized complexity priors for the skewness parameter of power links.  The  Canadian Journal of Statistics, 52(1), 98-117.

2023

• 141. M.S. Oliveira, D.C.R. Oliveira & Lachos, V.H. (2023). Influence diagnostics for skew-t censored linear regression models. Communications for Statistical Applications and Methods, 30(6), 605-629.
• 140. Benites*, L.S., Bolfarine, H. , Zeller, C.B. and Lachos, V.H. (2023). Regression modeling of censored data based on compound scale mixtures of normal distributions. Brazilian Journal of Probability and Statistics, 37(2), 282-312.
• 139. Lachos, V.H.,  Galea-Rojas, M. Prates, M. O. and  Zeller, C.B. (2023). Likelihood inference-based for mixed-effects models using the generalized hyperbolic distribution. Stat – An ISI’s journal, 12:e602.
• 138. Gomes*, J. C.,  Aoki, R., Lachos, V. H., Paula, G. A. and  Russo, C. M.  (2023). Fast inference for robust nonlinear mixed-effects models. Journal of Applied Statistics, 50:7, 1568-1591.
• 137. Valeriano*, K.L.,  Galarza, C.E., Matos, L.A. and Lachos, V.H. (2023). Likelihood-based inference for multivariate skew-t censored regression models.  Journal of Multivariate Analysis, 196, 105174.
• 136. da Paz*, R.  Bazan, J.L., Lachos, V.H. and  Dey, D. K.  (2023). A finite mixture mixed proportion regression model for classification problems in Longitudinal voting data.  Journal of Applied Statistics, 50,871-888.
  

2022

• 135.  de Alencar*, F.H.,  Matos, L.A. and Lachos, V.H. (2022). Finite mixture of censored linear mixed models for irregularly observed longitudinal data. Journal of Classification, 39 (3), 463 – 486.
• 134. Ferreira* , C.S., Bolfarine, H. and Lachos, V.H. (2022). Linear mixed models based on skew scale mixtures of normal distribution. Communications in Statistics – Simulation and Computation, 51, 7194–7214.
• 133. B. Mattos* , L.A. Matos and Lachos, V.H. (2022). Likelihood-based inference for mixed-effects models with censored response using skew-normal distribution. Springer International Publishing.  Edited volume in ‘Innovations in multivariate statistical modeling: navigating theoretical and multidisciplinary domains”. 1st ed. 2022
• 132. De Alencar* F.H., Galarza, C.E.  Matos, L.A. and Lachos V.H. (2022). Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution. Advances and Data Analysis and Classification, 16,  521–557.
• 131. Mattos*, B. Matos, L.A., Castro, L.M. and Lachos, V.H. (2022). Extending multivariate-t semiparametric mixed models for longitudinal data with censored responses and heavy-tails. Statistics in Medicine, 41, 3696-3719.
• 130.  Galarza* , C.E., Matos, L.A. and Lachos, V.H. (2022). An EM algorithm for estimating the parameters of the multivariate skew-normal distribution with censored responses. Metron, 80, 231–253.
• 129. Ordoñez, J., Galarza, C. E. and Lachos, V. H. (2022). An R package for censored spatial data analysis. Medwave. 22(S1): eCI54. DOI: 10.5867/Medwave.2022.S1.CI54.
• 128. Lachos, V.H., Bazan, J.L., Castro, L.M. and Park, J. (2022). The skew-t censored regression model: parameter estimation via an EM-type algorithm. Communications for Statistical Applications and Methods, 29, 1-29.
• 127. C.E. Galarza* , L.A. Matos, D.K. Dey & Lachos, V.H. (2022). On moments of folded and truncated multivariate extended skew-normal distributions. Journal of Computational and Graphical Statistics, 31, 455-465.
• 126. Galarza* , C.E. , Matos, L.A. and Lachos, V.H. (2022). Moments of the doubly truncated selection elliptical distributions with an emphasis on the unified multivariate skew-t distribution.  Journal of Multivariate Analysis, 189. 104944.

2021

• 125. Mattos* , B., Matos, L.A. and Lachos, V.H. (2021). A Semiparametric Mixed-Effects Model for Censored Longitudinal Data.  Statistical Methods in Medical Research, 30, 2582–2603.
• 124. Ruggeri, F.,  Bolfarine, H., Bazan, J.L., Arellano-Valle, R.B., Lachos, V.H. and  de Castro, M.  (2021). 2021 ISI Mahalanobis Award: a Tribute to Heleno Bolfarine. International Statistical Review,  89, 435–446.
• 123. Galarza* , C. Lachos, V.H. and Panpan, Z. (2021). Logistic quantile regression for bounded outcomes using a new family of heavy-tailed distributions. Sankhya B, 83, 325–349.
• 122.  Galarza*, C. E., Lin, T. I. , Wang, W. L. and Lachos, V. H. (2021). On moments of folded and truncated multivariate Student-t distributions based on recurrence relations.  Metrika, 84, 825–850.
• 121. Galarza* , C.E., Bourguignon, M. and Lachos, V.H. (2021). A skew-t quantile regression modeling for censored and missing data,  STAT, 10, 1-15 (e379).
• 120. R.C. Olivari* , A.M. Garay, Lachos, V.H. and Matos, L.A.  (2021). Autoregressive mixed-effects models for censored data. Journal of Biopharmaceutical Statistics, 31, 273-294.
• 119. Bandyopadhyay, D., Prates, M.O., Zhao*, X. and Lachos,V.H. (2021). Spatial skew-normal independent models for non-randomly missing clustered data.  Statistics in Medicine, 40, 3085-3105.
• 118. Schumacher* , F.L. Dey, D.K. and Lachos, V.H. (2021). Approximate inferences for nonlinear mixed-effects models with scale mixtures of skew-normal distributions.  Journal of Statistical Theory and Practice, 15, 60. (Part of a collection: Celebrating the Centenary of Professor C. R. Rao)
• 117. K.A.L. Valeriano*,  L.A. Matos and  Lachos, V.H.  (2021). Likelihood-based inference for spatio-temporal data with censored and missing responses.  Environmetrics, 32, e2663.
• 116. Lachos, V.H., M.O. Prates and D.K. Dey (2021). Heckman selection-t model: parameter estimation via the EM-algorithm. Preprint arXiv:2006.08036.  Journal of Multivariate Analysis, 184, 1-19.
• 115. Schumacher* , F. L., Lachos, V.H. and Matos, L.A. (2021). Scale mixture of skew-normal linear mixed models with within-subject serial dependence. Preprint arXiv:2002.01040. Statistics in Medicine, 40, 1790-1810.
• 114. Ye* , T. Lachos, V.H.,  Wang X. and   Dey,D.K. (2021). Comparisons of zero-augmented continuous regression models from a Bayesian perspective. Statistics in Medicine, 40, 1073-1100.
• 113. Nuñez* , M., Lachos, V.H.  and Matos, L. (2021). Estimation and diagnostic for partially censored regression models based on heavy-tailed distributions. Statistics and Its Interface, 14, 165–182.
• 112. Schumacher* , F. L., Ferreira, C.S., Prates, M.O., Lachos, A. and Lachos, V.H. (2021). A robust nonlinear mixed-effects model for COVID-19 death data.  Statistics and Its Interface, 14, 49–57.  Preprint arXiv:2007.00848

2020

• 111. Lachos, V.H., Cabral, CRB. and Garay* A.W.M. (2020). Moments of truncated scale mixtures of skew-normal distributions. Brazilian Journal of Probability and Statistics, 34, 478-494.
• 110.  Ferreira, C.S, Lachos, V.H. and Garay, A.M. (2020). Inference and diagnostics for heteroscedastic nonlinear regression models under skew scale mixtures of normal distributions. Journal of Applied Statistics, 47, 1690–1719.
• 109.  Galarza*. C.E, Lachos, V.H., Castro, M.C. and Louzada, N.F. (2020). Quantile regression for nonlinear mixed effects models: A likelihood based perspective. Statistical Papers,  61, 1281-1307.
• 108.  F. B. Goncalves, M. O. Prates and  Lachos, V.H. (2020). Robust Bayesian model selection for heavy-tailed linear regression models using mixtures. Brazilian Journal of Probability and Statistics, 34, 51-70.
• 107. Ramos*, P. L., Louzada, F., Dey, D. K. and Lachos, V. H. (2020). An Extended Poisson Family of Life Distribution: A Unified Approach in Competitive and Complementary Risks. Journal of Applied Statistics, 47, 306-322.

2019

• 106. Lachos, V.H., Benites* , L.B. and Maehara* , CRB. (2019). Linear regression models using finite mixtures of skew heavy-tailed distributions. The Chilean Journal of Statistics,  10, 21-41.
• 105. Matos*, L.A., Castro, LM.,  Lachos, V.H. and Lin, T-I (2019). Heavy-tailed longitudinal regression models for censored data: A robust parametric approach. TEST, 28, 844-878.
• 104. Wang, W. L., Castro, L. M. Lachos, V. H.  and Lin, T. I. (2019). Model-based clustering of censored data via mixtures of factor analyzers. Computational Statistics and Data Analysis, 140, 104-121.
• 103.  Luis M. Castro, Wan-Lun Wang, Lachos, V.H.,  Cristian L. Bayes and Vanda Inacio (2019). Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness. Statistical Methods in Medical Research, 28, 1457–1476.
• 102.  Zeller, C.B., Cabral, C.R.B. and Lachos, V.H. (2019). Finite mixture of regression models for censored data based on scale mixtures of normal distributions.  Advances in Data Analysis and Classification, 13, 89–116.
• 101. Lachos, V. H., Prates, M.O., Cabral, C.R.B. and Dey, D. K. (2019). Robust regression modeling for censored data based on mixtures of student-t distributions. Computational Statistics, 34, 123–152.
• 100. Lachos, V. H., Matos*, L. A., Castro, L. M. and Chen, M-H. (2019). Flexible longitudinal linear mixed models for multiple censored responses data.  Statistics in Medicine, 38, 1074-1102.

2018

• 99. Lachos, V.H., Cabral, C.R.B. and Zeller, C.B (2018). “FINITE MIXTURE OF SKEWED DISTRIBUTIONS”. SpringerBriefs in Statistics Series.
• 98.  Matos*, L.A., Lachos, V.H., Cabral, C.R.B. and L. M. Castro (2018). Multivariate measurement error models based on the t-distribution with censored responses. Statistics: A Journal of Theoretical and Applied Statistics, 52, 1395-1416.
• 97. Wan-Lun Wang,  Lachos, V.H. and Tsung-I Lin (2018). Multivariate longitudinal data analysis with censored and intermittent missing responses. Statistics in Medicine, 37, 2822-2835.
• 96.  Matos*, T. B., Lachos, V.H. and Garay, A.W.M. (2018). Likelihood based inference for censored linear regression models with scale mixtures of skew-normal distributions. Journal of Applied Statistics, 45,  2039-2066.
• 95.  Schumacher*, F. L., Lachos, V.H., Vilca-Labra, F.E. and Castro, L.M. (2018). Influence diagnostics for censored regression models with auto-regressive errors. Australian & New Zealand Journal of Statistics, 60,  209-229.
• 94.  Padilla*, J.L., Azevedo, C.L. and Lachos, V.H. (2018). Multidimensional multiple group IRT models with skew normal latent trait distributions. Journal of Multivariate Analysis, 167,  250-268.
• 93.   Wang, W-L, Lin, T-I and Lachos, V.H. (2018). Extending multivariate-t linear mixed models for multiple longitudinal data with censored responses and heavy tails. Statistical Methods in Medical Research, 27, 48-64.
• 92.  Ordoñez*, J. A., Lachos, V.H., Cabral, C.R.B, and Bandyopadhyay, D. (2018). Geo-statistical estimation and prediction for censored responses. Spatial Statistics, 23, 109-123.

2017

• 91.  Schumacher*, F. L., Lachos, V.H. and Dey, D.K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective.  Canadian Journal of Statistics, 45, 375–392.
• 90.  Lachos, V.H., Matos*, L.A., Barbosa*. T.S, Dey, DK. and Garay*, A.M. (2017). Influence diagnostics in spatial models with censored response.  Environmetrics, 28, 1-21. 
• 89.  Lachos, V. H., Moreno*, E. L. Cabral, C.R. and Kun, C.(2017). Finite mixture modeling of censored data using the multivariate Student-t distribution. Journal of Multivariate Analysis, 159, 151-167.
• 88.  Lachos, V.H., Dey, D., Cancho, V.G. and Louzada, N. (2017) . Scale mixtures log-Birnbaum-S8unders regression models with censored data: a Bayesian approach. Journal of Statistical Computation and Simulation, 87, 2002-2022.
• 87.  Galarza*, C.M.,  Lachos, V.H., Cabral, CRB. and Castro (2017). Robust Quantile Regression using a Generalized Class of Skewed Distributions. STAT, 113-130.
• 86.  Galvis*, D. M., Bandyopadhyay, D and Lachos, V. H. (2017). Augmented mixed models for clustered proportion data. Statistical Methods in Medical Research, 26, 880–897.
• 85.  Garay*, A. W, Castro, L.M., Leskow, L. and Lachos, V. H. (2017). Censored linear regression models for irregularly observed longitudinal data using the multivariate-t distribution . Statistical Methods in Medical Research, 26, 542–566.
• 84.  Massuia*, M.B., Garay*, AMG and Lachos, V.H. and Cabral, C. R. (2017). Bayesian analysis of censored linear regression models with scale mixtures of skew-normal distributions. Statistics and its Interface, 10, 425-439.
• 83.  Galarza*, C.M., Bandyopadhyay, D. and Lachos, V.H. (2017). Quantile regression for linear mixed models: A stochastic approximation EM approach. Statistics and its Interface, 10, 471-482.
• 82.  Garay*, A. W, Lachos, V. H., Bolfarine, H and Cabral, C.R. (2017). Linear censored regression models with scale mixtures of normal distributions . Statistical Papers, 58, 247–278.

2016

• 81.  Ferreira C.S. and Lachos, V. H.(2016). Nonlinear regression models under skew scale mixtures of normal distributions. Statistical Methodology, 33, 131–146.
• 80.  Matos*, L. A, Lachos, V. H. and Castro, L.M. (2016). Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads . TEST, 25, 627–653
• 79.  Gonzalez*, J. A. C., Lachos, V. H., Castro, L.M. and Patriota, A. (2016). A Confidence Set Analysis for Observed Samples: A Fuzzy Set Approach . Entropy, 18, 211-220.
• 78.  Blas*, B.G. Lachos, V. H. and Bolfarine, H. (2016). Heavy tailed calibration model with Berkson measurement errors for replicated data . Chemometrics and Intelligent Laboratory Systems, 156, 21-35
• 77.  Ferreira*, C. S., Lachos, V. H. and Bolfarine, H. (2016). Multivariate skew scale mixtures of normal distributions. Advances in Statistical Analysis, 100,421-441.
• 76.  Zeller*, C. B, Lachos, V. H. and Cabral, C.R. (2016). Robust mixture regression modelling based on scale mixtures of skew-normal distributions . TEST, 25, 375-396.
• 75.  Garay*, A. W, Lachos, V. H. and Lin, Tsung-I (2016). Nonlinear  censored regression models with scale mixtures of normal distributions . Statistics and its Interface,9, 281 — 293.

2015

• 74.  Galarza*, C.M. and Lachos, V.H. (2015). Likelihood based inference for quantile regression nonlinear mixed effects models. Estadistica, Inter-American Statistical Institute, 67, 33-74.
• 73.  Garay*, A. W, Lachos, V. H., Bolfarine, H and Cabral, C.R. (2015). Bayesian analysis of censored linear regression models with scale mixtures of normal distributions. Journal of Applied Statistics, 42, 2694-2714.
• 72.  Matos*, L. A., Bandyopadhyay, D., Castro, L. M. and Lachos, V. H. (2015). Influence diagnostics in mixed-effects models with censored data using the multivariate-t distribution. Journal of Multivariate Analysis, 141, 10-117.
• 71.  Garay*, A. M., Lachos, V. H., and Bolfarine, H. (2015). Bayesian zero-inflated negative binomial regression models: estimation and case influence diagnostics. Journal of Applied Statistics, 42, 1148-1165.
• 70.  Motta, M. R. , Galvis*, D. M., Lachos, V. H., and others (2015). A mixed-effect model for positive responses augmented by zeros . Statistics in Medicine, 34, 1761-1778.
• 69.  Bandyopadhyay, D. Castro, L.M., Lachos, V. H. and Pinheiro, H. P. (2015). Joint nonlinear mixed-effects models and diagnostics for censored HIV viral loads with CD4 measurement error. Journal of Agricultural, Biological, and Environmental Statistics, 20, 121-139.
• 68.  Costa*, D. R., Castro, L. M., Prates, M. and Lachos, V. H. (2015). Likelihood-based inference for Tobit confirmatory factor analysis using the multivariate t-distribution. Statistics and Computing, 25, 1163-1183.
• 67.  Lachos, V. H., Azevedo, C. L. N, Abanto-Valle, C. A. and Chen, M-H (2015). Quantile regression for censored mixed-effects models with applications to HIV studies. Statistics and its Interface, 8, 203-215.
• 66 .  Massuia*, M. B., Cabral, M. O, Matos, L.A. and Lachos, V.H.(2015). Influence diagnostics for Student-t censored linear regression models . Statistics- A Journal of Theoretical and Applied Statistics, 49, 1074-1094.
• 65.  Castro, L. M., Galvis*, D. M. and Lachos, V. H. (2015). Bayesian semiparametric linear mixed-effects models with normal/independent distributions. Chapman & Hall/CRC Press. Edited volume in -Current Trends in Bayesian Methodology with Applications-. Textbook – 516 Pages.
• 64.  Abanto-Valle, C. A., Lachos, V. H., and Dey, D.(2015). Bayesian estimation of a skew-t stochastic volatility model . Methodology & Computing in Applied Probability, 17, 721-738.

2014

• 63. Lachos*, V. H., and Labra, F. V. (2014). Multivariate skew-normal/independent distributions: properties and inference. Pro Mathematica, 28, 11–53.
• 62. Costa*, D. R, Lachos, V. H., Bazan, J. L. and Azevedo, C. L. N (2014). Estimation methods for multivariate Tobit confirmatory factor analysis. Computational Statistics and Data Analysis, 79, 248-260.
• 61.  Galvis*, D. M, Bandyopadhyay, D. and Lachos, V. H. (2014). Augmented mixed beta regression models for periodontal proportion data. Statistics in Medicine, 33, 3759-3771.
• 60.  Garay*, A. M., Lachos, V. H., Vilca, L. F. and Ortega, E. M. (2014). Statistical diagnostics for nonlinear regression models based on scale mixtures of skew-normal distributions. Journal of Statistical Computation and Simulation, 84, 1761-1778.
• 59.  Costa*, D. R, Lachos, V. H. and Prates, M.O. (2014). Generalized linear mixed models for correlated binary data with T-link . Statistics and Computing, 24,1111-1123.
• 58.  Zeller*, C. B., Lachos, V. H., Vilca, L. F. (2014). On estimation and influence diagnostics for the Grubbs’ model with asymmetric heavy-tailed distributions . Statistical Papers, 55, 671-690.
• 57.  Ferreira*, C. S, Lachos, V. H., and Bolfarine, H. (2014). Inference and diagnostics in skew scale mixtures of normal regression models . Journal of Statistical Computation and Simulation, 85, 517-537.
• 56.  Cabral, C. R., Lachos, V. H., and Zeller*, C.B.(2014). Multivariate measurement error models using finite mixtures of skew-student t distributions . Journal of Multivariate Analysis, 124, 179-198.
• 55.  Castro, L. M., Lachos, V. H., Ferreira, G. and Arellano-Valle, R. (2014). Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach . Statistical Methodology, 18, 14-31.

2013

• 54.  Ferreira, G., Castro, L. M., Lachos, V. H., and Dias, R. (2013). Bayesian modeling of autoregressive partial linear models with scale mixture of normal errors. Journal of Applied Statistics, 40, 1796-1816.
• 53.  Prates*, M. O., Cabral, M. O, and Lachos, V.H.(2013). Fitting finite mixture of scale mixture of skew-normal distributions . Journal of Statistical Software, 54, 1-20.
• 52.  Lachos, V. H., Castro, L. M. and Dey, D.K. (2013). Bayesian inference in nonlinear mixed-effects models using normal independent distributions . Computational Statistics and Data Analysis, 64, 237-252.
• 51.  Matos*, L. A., Lachos, V. H., Balakrishnan, N. and Vilca-Labra, F.(2013). Influence diagnostics in linear and nonlinear mixed-effects models with censored data . Computational Statistics and Data Analysis, 57, 450-464.
• 50.  Matos*, L. A., Prates*, M. O., Chen, M-H. and Lachos, V. H. (2013). Likelihood based inference for linear and nonlinear mixed-effects models with censored response using the multivariate-t distribution . Statistica Sinica, 23, 1299-1322.
• 49.  Blás*, B.G.,  Lachos, V. H. and Bolfarine, H. (2013). Statistical analysis of controlled calibration model with replicates . Journal of Statistical Computation and Simulation, 83, 941-961.

2012

• 48.  Abanto-Valle, C. A., Lachos, V.H. and Ghosh, P. (2012). A Bayesian term structure modeling using heavy-tailed distributions. Applied Stochastic Models in Business and Industry, 28, 430-447.
• 47.  Prates*, M. O., Dey, D. K, and Lachos, V.H. (2012). A dengue fever study in the state of Rio de Janeiro with the use of generalized skew-normal/independent spatial fields. The Chilean Journal of Statistics, 3, 33-45.
• 46.  Zeller*, C. B., Carvalho*, R. R, and Lachos, V. H. (2012). On diagnostics for multivariate measurement error model with asymmetric heavy-tailed distributions. Statistical Papers, 53, 665-683.
• 45.  Abanto-Valle, C. A., Migon, H. S. and Lachos, V. H.(2012). Stochastic volatility in mean models with heavy-tailed distributions. Brazilian Journal of Probability and Statistics, 26, 402-422.
• 44.  Lachos, V.H. , Bandyopadhyay, D., Castro, L.M.C and Dey, D. K.(2012). Skew-normal/independent linear mixed models for censored responses with applications to HIV viral loads. Biometrical Journal, 405-425, 2012.
• 43.  Lachos, V. H., Garay*, A. M. , Ortega, E. M. and Vilca, L. F. (2012). Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions. Journal of Statistical Planning and Inference, 142, 2149-2165.
• 42.  Barbosa-Cabral, C. R., Lachos, V. H. and Madruga, R. M. (2012). Bayesian skew-normal independent linear mixed models with heterogeneity in the random-effects population. Journal of Statistical Planning and Inference, 212, 181-200.
• 41.  Cabral, C. R., Lachos, V. H., and Prates*, M.O (2012). Robust multivariate mixture modelling using scale mixtures of skew-normal distributions. Computational Statistics and Data Analysis, 56, 226-246.
• 40.  Lachos, V. H., Cabral, C. R. and Abanto-Valle, C. A. (2012). A noniterative sampling Bayesian method for linear mixed models with normal independent distributions. Journal of Applied Statistics, 39, 531-549.

2011

• 39.  Lachos, V.H., Bandyopadhyay D. and Dey D. K. (2011) Linear and non-linear mixed-effects models for censored HIV viral loads using normal/independent distributions. Biometrics, 55, 1304-1318.
• 38.  Lachos, V. H., Bandyopadhyay, D. and A. M. Garay* (2011). Heteroscedastic nonlinear regression models based on scale mixtures of skew normal distributions. Statistics and Probability Letter, 81, 1208-1217.
• 37.  Garay*, A. M., Ortega, E. M., and Lachos, V. H. (2011). On estimation and influence diagnostics for zero-inflated negative binomial regression models. Computational Statistics and Data Analysis, 55, 1304-1318
• 36.  Zeller*, C. B., Lachos, V. H, and Labra, F. (2011). Local influence analysis for regression models with skew-normal independent distributions. Journal of Applied Statistics, 38, 343 – 368.
• 35.  Abanto-Valle, C. A., Migon, H. and Lachos, V.H. (2011). Bayesian analysis of heavy-tailed stochastic volatility in mean model using scale mixtures of normal distributions. Journal of Statistical Planning and Inference, 141, 1875-1887.
• 34.  Ferreira*, C. S., Lachos, V. H. and Bolfarine, H. (2011). Skew scale mixtures of normal distributions: properties and estimation. Statistical Methodology, 8, 154-171.
• 33.  Lachos, V. H., Abanto-Valle, C. A. and Angolini*, T (2011). On estimation and local influence analysis for measurement errors models under heavy-tailed distributions . Statistical Papers, 52, 567-590.
• 32.  Garay*, A. M., Lachos, V. H., and Abanto-Valle, C.A. (2011). Nonlinear regression models based on scale mixtures of skew-normal distributions. Journal of the Korean Statistical Society, 40, 115-124.
• 31.  Lachos, V.H., Filidor, V.L., Garibay, V.C. and Aoky R. (2011). Skew-normal distribution in multivariate null intercept measurement error models. Brazilian Journal of Probability and Statistics, 25, 145-170.

2010

• 30.  Lachos, V. H., Ghosh, P. and Arellano-Valle R. B. (2010). Likelihood based inference for skew-normal/independent linear mixed model. Statistica Sinica, 20, 303-322.
• 29.  Lachos, V.H., Bolfarine H., Vilca, L.F. and Ghosh, P. (2010). Robust multivariate measurement error models with scale mixtures of skew-normal distributions. Statistics (A Journal of Theoretical and Applied Statistics), 44, 541-556.
• 28.  Abanto-Valle, C. A., Bandyopadhyay, D, Lachos, V. H. (2010). Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions . Computational Statistics and Data Analysis, 54, 2883-2898.
• 27.  Bandyopadhyay, D, Lachos, V. H. Abanto-Valle, C. A. (2010). Bayesian inference for bivariate skew-normal/independent linear mixed models with application to periodontal disease. Statistics in Medicine, 29, 2643-2655.
• 26.  Zeller*, C. B., Lachos, V. H., Vilca, L. F. and Balakrishnan, N. (2010). Influence analyses of skew-normal/independent linear mixed models. Computational Statistics and Data Analysis, 54, 1266-1280.
• 25.  Basso*, R., Lachos, V. H, and Cabral, C.R.B (2010). Robust mixture modelling using scale mixtures of skew-normal distributions. Computational Statistics and Data Analysis, 54, 2926-2941.
• 24.  Cancho, V. G., Dey, K. D., Lachos, V. H. and Andrade, M. (2010). Bayesian nonlinear regression models with scale mixtures of skew normal distributions: Estimation and case influence diagnostics. Computational Statistics and Data Analysis, 55, 588-602.
• 23.  Garibay, V. Aoki, R. and Lachos, V.H. (2010). Bayesian analysis for skew-t multivariate null intercept measurement error models. Statistical Papers, 51,531-545.
• 22.  Garibay, V. Ortega, E. and Lachos, V.H. (2010). Skew-normal comparative calibration models. Journal of Statistical Theory and Applications, 9, 143-168.
• 21.  Lachos, V. H., Garibay, V. and Ortega, E. (2010). A nonlinear model with skew-normal errors. Statistical Papers, 51, 547-558.
• 20.  Lachos, V.H., Bolfarine H. and Montenegro*, L.C. (2010). Inference for a Skew Extension of the Grubbs Model. Statistical Papers, 51, 701-715.

2009

• 19.  Montenegro*, L.C, Lachos, V.H. and Bolfarine H. (2009). Local influence analysis of skew-normal linear mixed models. Communication in Statistics- Theory and Methods, 38, 484-496.
• 18.  Montenegro*, L.C., Bolfarine, H. and Lachos, V.H. (2009). Influence diagnostics for a skew extension of the Grubbs model. Communication in Statistics- Simulation and Computation, 38, 667-681.
• 17.  Lachos, V. H., Dey, K. D. and Cancho, V. G. (2009). Robust linear mixed models with skew-normal independent distributions from a Bayesian perspective. Journal of Statistical Planning and Inference, 139, 4098-4110.
• 16.  Ghosh, P., Bayes*, C. R. and Lachos, V. H. (2009). A robust Bayesian approach to null intercept measurement error model with application to dental data. Computational Statistics and Data Analysis, 53, 1066-1079.
• 15.  Lachos, V. H., Vilca, F. L,, Garibay, V. and Aoki, R. (2009). Robust multivariate measurement error Model with skew-normal/independent distributions and Bayesian MCMC implementation. Statistical Methodology, 6, 527-541
• 14.  Ortega, E. M., Garibay, V., and Lachos, V. H. (2009). A generalized log-gamma mixture model with long-term survivors: sensitivity analysis. Sankhya – Indian Statistical Institute, Series B, 71, 1-29.

2008

• 13.  Lachos, V.H., Bolfarine H. e Montenegro*, L.C (2008). Inference and assessment of local influence in skew-normal null intercept measurement error models. Journal of Statistical Computation and Simulation, 78, 395-419.
• 12.  Ortega, E., Garibay, V. and Lachos, V.H. (2008). Influence diagnostics in the Weibull mixture model with covariates. Statistics and Operations Research Transactions (SORT), 115-140, 2008.
• 11.  Aoki, R. Garibay, V. and Lachos, V.H. (2008). Bayesian analysis for a skew extension of the multivariate null intercept measurement error model. Journal of Applied Statistics, 35, 1239-1251.
• 10.  Lachos, V. H. (2008). Scale mixtures of skew-normal distribution with applications in regression models. Estadistica (Inter-American Statistical Institute), 60, 1-28.

2007

• 9.  Lachos, V.H., Bolfarine H. and Montenegro*, L.C. (2007). Influence diagnostics for skew-normal linear mixed models. Sankhya – Indian Statistical Institute, 69, 648-670.
• 8.  Lachos, V.H., Vilca, L.F. and Galea, M. (2007). Influence diagnostics for Grubbs’s model. Statistical Papers, 48, 419-436.
• 7.  Lachos, V.H. and Bolfarine H. (2007). Skew-probit measurement error models. Statistical Methodology, 4, 1-12.
• 6.  Lachos, V.H., Bolfarine H., Arellano-Valle, R.B. and Montenegro*, L.C. (2007). Likelihood based inference for multivariate skew-normal regression models. Communication in Statistics: Theory and methods (Special Issue in skew distributions), 36, 1769-1786.
• 5.  Arellano-Valle, R.B., Bolfarine, H. and Lachos, V.H. (2007). Bayesian inference for skew-normal linear mixed models. Journal of Applied Statistics, 34, 663-682.

2006

• 4.  Lachos, V.H. and Bolfarine H. (2006). Skew binary regression with measurement errors. Statistics (A Journal of Theoretical and Applied Statistics), 40, 485-494.

2005

• 3.  Arellano-Valle, R.B., Ozan, S., Bolfarine, H. and Lachos, V.H. (2005). Skew-normal measurement error models. Journal of Multivariate Analysis, 96, 93-116.
• 2.  Arellano-Valle, R.B., Bolfarine, H. and Lachos, V.H. (2005). Skew-normal linear mixed models. Journal of Data Science, 4, 415-438.

2004 (PhD defense)

• 1.  Lachos, V.H. (2004). Modelos Lineares Mistos Assimétricos. Ph.D. Thesis, Inst. of Mathematics and Statistics, University of São Paulo, Brazil, 160 p. (Wednesday, 10/27/2004).

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Packages for R

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1. mixsmsn: Fitting finite mixture of scale mixture of skew-normal distributions (2010)
2. tlmec: Linear Student-t Mixed-Effects Models with Censored Data (2011)
3. nlsmsn: Fitting univariate non-linear scale mixture of skew-normal regression models. (2012)
4. CensRegMod: Fitting Normal and Student-t censored regression models. (2012)
5. SMNCensReg: Fitting univariate censored regression model under the scale mixture of normal distributions. (2013)
6. ALDqr: Quantile Regression Using Asymmetric Laplace Distribution. (2013)
7. BayesCR: Bayesian analysis of censored linear regression models with scale mixtures of normal (SMN) distributions (2013)
8. qrLMM: Quantile Regression for Linear Mixed-Effects Models (2015)
9. ald: The Asymmetric Laplace Distribution (2015)
10. CensMixReg: Censored Linear Mixture Regression Models (2015)
11. lqr: Robust Linear Quantile Regression (2016)
12. FMsmsnReg: Regression Models with Finite Mixtures of Skew Heavy-Tailed Errors (2016)
13. ARCensReg: Fitting Univariate Censored Linear Regression Model with Autoregressive Errors (2016)
14. CensSpatial: Censored Spatial Models (2016)
15. MomTrunc: Moments of Folded and Doubly Truncated Multivariate Distributions (2018)
16. PartCensReg: Partially Censored Regression Models Based on Heavy-Tailed Distributions (2018)
17. StempCens: Spatio-Temporal Estimation and Prediction for Censored/Missing Responses (2019)
18. CensMFM: Finite Mixture of Multivariate Censored/Missing Data (2019)
19. skewlmm: Scale Mixture of Skew-Normal Linear Mixed Models (2020)
20. OBASpatial: Objective Bayesian Analysis for Spatial Regression Models (2020)
21. FMCensSkewReg: Finite Mixture of Censored Regression Models with Skewed Distributions (2026)
22. ZIHINAR1: Zero-Inflated and Hurdle INAR(1) Models (2026)
23. HeckmanEM: Fit Normal, Student-t or Contaminated Normal Heckman Selection Models (2026)
24. HeckmanStan: Heckman Selection Models Based on Bayesian Analysis (2026).
25. EMGCR: Fit a Mixture Cure Rate Model with Custom Link Function (2026).

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Submitted/in Progress

1. Galarza, C. and Lachos, V.H. (2024). Finite mixture modeling of censored and missing data using the multivariate skew-t distribution (In progress).
2. D. Liu & V.H. Lachos (2024). The use of the EM algorithm in high-dimensional linear mixed-effects models with L1-like penalties (In progress).
3. Lachos, V.H. (2025). Multivariate Contaminated Skew-Normal Censored Model: Properties and Maximum Likelihood Inference (In progress).
4. Brisilda Nbreka, D. Dey and  Lachos, V.H. (2025). Quantifying homophily through skewed link functions in Bayesian network models: Estimating peer influence ( Submitted).
5. J. da Silva Pereira, C.A.R. Diniz & V.H. Lachos (2025). Matrix-Variate Shifted Generalized Asymmetric Laplace Distribution ( Submitted).
6. W-L. Wang, V.H. Lachos, P-T. Ho & T-I. Lin (2025). Maximum Likelihood Estimation for Mixtures of Truncated Multivariate Normal Distributions with Missing Information. ( Submitted).
7. S.D. Tomarchio & V.H. Lachos (2025).  Finite Mixtures of Matrix-Variate Normal Distributions on Censored and Missing Data. ( Submitted).
8. L. Benites, C.B. Zeller & V.H. Lachos (2025). Quantile Regression using the Asymmetric Laplace Distribution: Estimation and Case-Deletion Diagnostics. ( Submitted).
9. C. Yoo, D.K. Dey, J.M.F. Carrasco & V.H. Lachos (2025). Mixture Cure Models with Flexible Survival and Link Functions: Parameter Estimation via the EM Algorithm.( Submitted).
10. C.A.R. Diniz, V.E. Lachos & V.H. Lachos (2025). Matrix-Variate Regression Model for Multivariate Spatio-Temporal Data.( Submitted). 
    
11. A.P. Correira, C.A.R. Diniz, L.M. Castro & V.H. Lachos (2025). An EM Algorithm for Fitting Matrix-Variate Skew-Normal Distributions of Interval-Censored and Missing Data. ( Submitted).
12. J.A. Ordoñez, T-I. Lin, V.H. Lachos & L.M. Castro (2025).  A Unified Spatiotemporal Framework for Modeling Censored and Missing Areal Responses. ( Submitted).
13. Miranda-Paiva, C., Ortega, E., and Lachos, V.H. (2026). A new bivariate regression and machine learning with application to math anxiety data. ( Submitted).